scholarly journals Surrogate Model-Based Uncertainty Quantification for a Helical Gear Pair

2021 ◽  
pp. 191-207
Author(s):  
Thomas Diestmann ◽  
Nils Broedling ◽  
Benedict Götz ◽  
Tobias Melz
Keyword(s):  
Uncertainty Quantification ◽  
Large Scale ◽  
Early Stage ◽  
Gaussian Process Regression ◽  
System Size ◽  
Transmission Systems ◽  
Dynamic Simulations ◽  
Drive State ◽  
Early Stage Design ◽  
Noise Vibration

AbstractCompetitive industrial transmission systems must perform most efficiently with reference to complex requirements and conflicting key performance indicators. This design challenge translates into a high-dimensional multi-objective optimization problem that requires complex algorithms and evaluation of computationally expensive simulations to predict physical system behavior and design robustness. Crucial for the design decision-making process is the characterization, ranking, and quantification of relevant sources of uncertainties. However, due to the strict time limits of product development loops, the overall computational burden of uncertainty quantification (UQ) may even drive state-of-the-art parallel computing resources to their limits. Efficient machine learning (ML) tools and techniques emphasizing high-fidelity simulation data-driven training will play a fundamental role in enabling UQ in the early-stage development phase.This investigation surveys UQ methods with a focus on noise, vibration, and harshness (NVH) characteristics of transmission systems. Quasi-static 3D contact dynamic simulations are performed to evaluate the static transmission error (TE) of meshing gear pairs under different loading and boundary conditions. TE indicates NVH excitation and is typically used as an objective function in the early-stage design process. The limited system size allows large-scale design of experiments (DoE) and enables numerical studies of various UQ sampling and modeling techniques where the design parameters are treated as random variables associated with tolerances from manufacturing and assembly processes. The model accuracy of generalized polynomial chaos expansion (gPC) and Gaussian process regression (GPR) is evaluated and compared. The results of the methods are discussed to conclude efficient and scalable solution procedures for robust design optimization.

scholarly journals Biased Information Passing Between Subsystems Over Time in Complex System Design

2015 ◽  
Vol 138 (1) ◽  
Author(s):  
Jesse Austin-Breneman ◽  
Bo Yang Yu ◽  
Maria C. Yang
Keyword(s):  
Complex System ◽  
System Design ◽  
Large Scale ◽  
Early Stage ◽  
Systems Design ◽  
System Level ◽  
Worst Case ◽  
Early Stage Design ◽  
Complex System Design ◽  
Biased Information

During the early stage design of large-scale engineering systems, design teams are challenged to balance a complex set of considerations. The established structured approaches for optimizing complex system designs offer strategies for achieving optimal solutions, but in practice suboptimal system-level results are often reached due to factors such as satisficing, ill-defined problems, or other project constraints. Twelve subsystem and system-level practitioners at a large aerospace organization were interviewed to understand the ways in which they integrate subsystems in their own work. Responses showed subsystem team members often presented conservative, worst-case scenarios to other subsystems when negotiating a tradeoff as a way of hedging against their own future needs. This practice of biased information passing, referred to informally by the practitioners as adding “margins,” is modeled in this paper with a series of optimization simulations. Three “bias” conditions were tested: no bias, a constant bias, and a bias which decreases with time. Results from the simulations show that biased information passing negatively affects both the number of iterations needed and the Pareto optimality of system-level solutions. Results are also compared to the interview responses and highlight several themes with respect to complex system design practice.

scholarly journals Efficient uncertainty quantification and management in the early stage design of composite applications

2020 ◽  
Vol 251 ◽  
pp. 112538 ◽  
Author(s):  
Dinesh Kumar ◽  
Yao Koutsawa ◽  
Gaston Rauchs ◽  
Mariapia Marchi ◽  
Carlos Kavka ◽  
...  
Keyword(s):  
Uncertainty Quantification ◽  
Early Stage ◽  
Stage Design ◽  
Early Stage Design

scholarly journals Biased Information Passing Between Subsystems Over Time in Complex System Design

2014 ◽  
Author(s):  
Jesse Austin-Breneman ◽  
Bo Yang Yu ◽  
Maria C. Yang
Keyword(s):  
Complex System ◽  
System Design ◽  
Large Scale ◽  
Early Stage ◽  
System Level ◽  
Worst Case ◽  
Team Members ◽  
Early Stage Design ◽  
Complex System Design ◽  
Biased Information

The early stage design of large-scale engineering systems challenges design teams to balance a complex set of considerations. Established structured approaches for optimizing complex system designs offer strategies for achieving optimal solutions, but in practice sub-optimal system-level results are often reached due to factors such as satisficing, ill-defined problems or other project constraints. Twelve sub-system and system-level practitioners at a large aerospace organization were interviewed to understand the ways in which they integrate sub-systems. Responses showed sub-system team members often presented conservative, worst-case scenarios to other sub-systems when negotiating a trade-off as a way of hedging their own future needs. This practice of biased information passing, referred to informally by the practitioners as adding “margins,” is modeled with a series of optimization simulations. Three “bias” conditions were tested: no bias, a constant bias and a bias which decreases with time. Results from the simulations show that biased information passing negatively affects both the number of iterations needed to reach and the Pareto optimality of system-level solutions. Results are also compared to the interview responses and highlight several themes with respect to complex system design practice.

The Role of Idea Fluency and Timing on Highly Innovative Design Concepts

2020 ◽  
Author(s):  
Yakira Mirabito ◽  
Kosa Goucher-Lambert
Keyword(s):  
Large Scale ◽  
Design Research ◽  
Early Stage ◽  
Deep Understanding ◽  
Concept Generation ◽  
Stage Design ◽  
Single Measure ◽  
Design Concepts ◽  
Early Stage Design ◽  
The Impact

Abstract Ongoing work within the engineering design research community seeks to develop automated design methods and tools that enhance the natural capabilities of designers in developing highly innovative concepts. Central to this vision is the ability to first obtain a deep understanding of the underlying behavior and process dynamics that predict successful performance in early-stage concept generation. The objective of this research is to better understand the predictive factors that lead to improved performance during concept generation. In particular, this work focuses on the impact of idea fluency and timing of early-stage design concepts, and their effect on overall measures of ideation session success. To accomplish this, we leverage an existing large-scale dataset containing hundreds of early-stage design concepts; each concept contains detailed ratings regarding its overall feasibility, usefulness, and novelty, as well as the completion time of each idea. Surprisingly, results indicate that there is no effect of idea fluency or timing on the quality of the output when using a holistic evaluation mechanism, such as the innovation measure, instead of a single measure such as novelty. Thus, exceptional concepts can be achieved by all generator segments independent of idea fluency. Furthermore, in early-stage concept generation sessions, highest-rated concepts have an equal probability of occurring early and late in a session. Taken together, these findings can be used to improve performance in ideation by effectively determining when and which types of design interventions future design tools might suggest.

Factors Impacting Highly Innovative Designs: Idea Fluency, Timing, and Order

2021 ◽  
pp. 1-29
Author(s):  
Yakira Mirabito ◽  
Kosa Goucher-Lambert
Keyword(s):  
Large Scale ◽  
Design Research ◽  
Early Stage ◽  
Deep Understanding ◽  
Concept Generation ◽  
Stage Design ◽  
Single Measure ◽  
Design Concepts ◽  
Early Stage Design ◽  
The Impact

Abstract Ongoing work within the engineering design research community seeks to develop automated design methods and tools that enhance the natural capabilities of designers in developing highly innovative concepts. Central to this vision is the ability to first obtain a deep understanding of the underlying behavior and process dynamics that predict successful performance in early-stage concept generation. The objective of this research is to better understand the predictive factors that lead to improved performance during concept generation. In particular, this work focuses on the impact of idea fluency and timing of early-stage design concepts, and their effect on overall measures of ideation session success. To accomplish this, we leverage an existing large-scale dataset containing hundreds of early-stage design concepts; each concept contains detailed ratings regarding its overall feasibility, usefulness, and novelty, as well as when in the ideation session the idea was recorded. Surprisingly, results indicate that there is no effect of idea fluency or timing on the quality of the output when using a holistic evaluation mechanism, such as the innovation measure, instead of a single measure such as novelty. Thus, exceptional concepts can be achieved by all participant segments independent of idea fluency. Furthermore, in early-stage concept generation sessions, highest-rated concepts have an equal probability of occurring early and late in a session. Taken together, these findings can be used to improve performance in ideation by effectively determining when and which types of design interventions future design tools might suggest.

Konkurrenz zwischen Stieleiche und Buche auf Lothar-Sturmflächen | Competition between pedunculate oak and European beech on Lothar windthrow areas

2009 ◽  
Vol 160 (5) ◽  
pp. 114-123 ◽  
Author(s):  
Daniel Otto ◽  
Sven Wagner ◽  
Peter Brang
Keyword(s):  
Large Scale ◽  
Early Stage ◽  
Tree Height ◽  
European Beech ◽  
Threshold Value ◽  
Test Methods ◽  
Permanent Plots ◽  
Pedunculate Oak ◽  
Competitive Pressure ◽  
Competition Index

The competitive pressure of naturally regenerated European beech (Fagus sylvatica) saplings on planted pedunculate oak (Quercus robur) was investigated on two 1.8 ha permanent plots near Habsburg and Murten (Switzerland). The plots were established with the aim to test methods of artificial oak regeneration after large-scale windthrow. On both plots, 80 oaks exposed to varying levels of competitive pressure from at most 10 neighbouring beech trees were selected. The height of each oak as well as stem and branch diameters were measured. The competitive pressure was assessed using Schütz's competition index, which is based on relative tree height, crown overlap and distance from competing neighbours. Oak trees growing without or with only slight competition from beech were equally tall, while oaks exposed to moderate to strong competition were smaller. A threshold value for the competition index was found above which oak height decreased strongly. The stem and branch diameters of the oaks started to decrease even if the competition from beech was slight, and decreased much further with more competition. The oak stems started to become more slender even with only slight competition from beech. On the moderately acid beech sites studied here, beech grow taller faster than oak. Thus where beech is competing with oak and the aim is to maintain the oak, competitive pressure on the oak must be reduced at an early stage. The degree of the intervention should, however, take the individual competitive interaction into account, with more intervention if the competition is strong.

scholarly journals Tensor-structured algorithm for reduced-order scaling large-scale Kohn–Sham density functional theory calculations

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Chih-Chuen Lin ◽  
Phani Motamarri ◽  
Vikram Gavini
Keyword(s):  
Density Functional Theory ◽  
Density Functional ◽  
Large Scale ◽  
Density Functional Theory Calculations ◽  
System Size ◽  
Real Space ◽  
Functional Theory ◽  
Reduced Order ◽  
Separable Approximation ◽  
Tensor Basis

AbstractWe present a tensor-structured algorithm for efficient large-scale density functional theory (DFT) calculations by constructing a Tucker tensor basis that is adapted to the Kohn–Sham Hamiltonian and localized in real-space. The proposed approach uses an additive separable approximation to the Kohn–Sham Hamiltonian and an L1 localization technique to generate the 1-D localized functions that constitute the Tucker tensor basis. Numerical results show that the resulting Tucker tensor basis exhibits exponential convergence in the ground-state energy with increasing Tucker rank. Further, the proposed tensor-structured algorithm demonstrated sub-quadratic scaling with system-size for both systems with and without a gap, and involving many thousands of atoms. This reduced-order scaling has also resulted in the proposed approach outperforming plane-wave DFT implementation for systems beyond 2000 electrons.

scholarly journals On Randomized Trace Estimates for Indefinite Matrices with an Application to Determinants

2021 ◽  
Author(s):  
Alice Cortinovis ◽  
Daniel Kressner
Keyword(s):  
Large Scale ◽  
Gaussian Process Regression ◽  
Likelihood Estimation ◽  
Random Vectors ◽  
Symmetric Positive Definite ◽  
Matrix Logarithm ◽  
The Matrix ◽  
Trace Estimation ◽  
Scale Matrix ◽  
Existing Result

AbstractRandomized trace estimation is a popular and well-studied technique that approximates the trace of a large-scale matrix B by computing the average of $$x^T Bx$$ x T B x for many samples of a random vector X. Often, B is symmetric positive definite (SPD) but a number of applications give rise to indefinite B. Most notably, this is the case for log-determinant estimation, a task that features prominently in statistical learning, for instance in maximum likelihood estimation for Gaussian process regression. The analysis of randomized trace estimates, including tail bounds, has mostly focused on the SPD case. In this work, we derive new tail bounds for randomized trace estimates applied to indefinite B with Rademacher or Gaussian random vectors. These bounds significantly improve existing results for indefinite B, reducing the number of required samples by a factor n or even more, where n is the size of B. Even for an SPD matrix, our work improves an existing result by Roosta-Khorasani and Ascher (Found Comput Math, 15(5):1187–1212, 2015) for Rademacher vectors. This work also analyzes the combination of randomized trace estimates with the Lanczos method for approximating the trace of f(B). Particular attention is paid to the matrix logarithm, which is needed for log-determinant estimation. We improve and extend an existing result, to not only cover Rademacher but also Gaussian random vectors.

scholarly journals Favorable Effects of GLP-1 Receptor Agonist against Pancreatic β-Cell Glucose Toxicity and the Development of Arteriosclerosis: “The Earlier, the Better” in Therapy with Incretin-Based Medicine

2021 ◽  
Vol 22 (15) ◽  
pp. 7917
Author(s):  
Hideaki Kaneto ◽  
Tomohiko Kimura ◽  
Masashi Shimoda ◽  
Atsushi Obata ◽  
Junpei Sanada ◽  
...  
Keyword(s):  
Large Scale ◽  
Cell Function ◽  
Apoptotic Cell ◽  
Early Stage ◽  
Insulin Biosynthesis ◽  
Protective Effects ◽  
Pancreatic Β Cell ◽  
Β Cells ◽  
Β Cell ◽  
Glucose Toxicity

Fundamental pancreatic β-cell function is to produce and secrete insulin in response to blood glucose levels. However, when β-cells are chronically exposed to hyperglycemia in type 2 diabetes mellitus (T2DM), insulin biosynthesis and secretion are decreased together with reduced expression of insulin transcription factors. Glucagon-like peptide-1 (GLP-1) plays a crucial role in pancreatic β-cells; GLP-1 binds to the GLP-1 receptor (GLP-1R) in the β-cell membrane and thereby enhances insulin secretion, suppresses apoptotic cell death and increase proliferation of β-cells. However, GLP-1R expression in β-cells is reduced under diabetic conditions and thus the GLP-1R activator (GLP-1RA) shows more favorable effects on β-cells at an early stage of T2DM compared to an advanced stage. On the other hand, it has been drawing much attention to the idea that GLP-1 signaling is important in arterial cells; GLP-1 increases nitric oxide, which leads to facilitation of vascular relaxation and suppression of arteriosclerosis. However, GLP-1R expression in arterial cells is also reduced under diabetic conditions and thus GLP-1RA shows more protective effects on arteriosclerosis at an early stage of T2DM. Furthermore, it has been reported recently that administration of GLP-1RA leads to the reduction of cardiovascular events in various large-scale clinical trials. Therefore, we think that it would be better to start GLP-1RA at an early stage of T2DM for the prevention of arteriosclerosis and protection of β-cells against glucose toxicity in routine medical care.

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